IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i20p8752-d432653.html
   My bibliography  Save this article

Built Environment Correlates of the Propensity of Walking and Cycling

Author

Listed:
  • Longzhu Xiao

    (Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China)

  • Linchuan Yang

    (Department of Urban and Rural Planning, School of Architecture and Design, Southwest Jiaotong University, Chengdu 611756, China)

  • Jixiang Liu

    (Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong, Hong Kong, China)

  • Hongtai Yang

    (School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 611756, China)

Abstract

Walking and cycling are not only frequently-used modes of transport but also popular physical activities. They are beneficial to traffic congestion mitigation, air pollution reduction, and public health promotion. Hence, examining and comparing the built environment correlates of the propensity of walking and cycling is of great interest to urban practitioners and decision-makers and has attracted extensive research attention. However, existing studies mainly look into the two modes separately or consider them as an integral (i.e., active travel), and few compare built environment correlates of their propensity in a single study, especially in the developing world context. Thus, this study, taking Xiamen, China, as a case, examines the built environment correlates of the propensity of walking and cycling simultaneously and compares the results wherever feasible. It found (1) built environment correlates of the propensity of walking and cycling differ with each other largely in direction and magnitude; (2) land use mix, intersection density, and bus stop density are positively associated with walking propensity, while the distance to the CBD (Central Business District) is a negative correlate; (3) as for cycling propensity, only distance to CBD is a positive correlate, and job density, intersection density, and bus stop density are all negative correlates. The findings of this study have rich policy implications for walking and cycling promotion interventions.

Suggested Citation

  • Longzhu Xiao & Linchuan Yang & Jixiang Liu & Hongtai Yang, 2020. "Built Environment Correlates of the Propensity of Walking and Cycling," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8752-:d:432653
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/20/8752/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/20/8752/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Reid Ewing & Robert Cervero, 2010. "Travel and the Built Environment," Journal of the American Planning Association, Taylor & Francis Journals, vol. 76(3), pages 265-294.
    2. Cynthia Chen & Hongmian Gong & Robert Paaswell, 2008. "Role of the built environment on mode choice decisions: additional evidence on the impact of density," Transportation, Springer, vol. 35(3), pages 285-299, May.
    3. van den Berg, Pauline & Sharmeen, Fariya & Weijs-Perrée, Minou, 2017. "On the subjective quality of social Interactions: Influence of neighborhood walkability, social cohesion and mobility choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 309-319.
    4. Arlie Adkins & Carrie Makarewicz & Michele Scanze & Maia Ingram & Gretchen Luhr, 2017. "Contextualizing Walkability: Do Relationships Between Built Environments and Walking Vary by Socioeconomic Context?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 83(3), pages 296-314, July.
    5. Yena Song & John M Preston & Christian Brand, 2013. "What Explains Active Travel Behaviour? Evidence from Case Studies in the UK," Environment and Planning A, , vol. 45(12), pages 2980-2998, December.
    6. Reid Ewing & Guang Tian & JP Goates & Ming Zhang & Michael J Greenwald & Alex Joyce & John Kircher & William Greene, 2015. "Varying influences of the built environment on household travel in 15 diverse regions of the United States," Urban Studies, Urban Studies Journal Limited, vol. 52(13), pages 2330-2348, October.
    7. Cao, Xinyu, 2006. "The Causal Relationship between the Built Environment and Personal Travel Choice: Evidence from Northern California," University of California Transportation Center, Working Papers qt07q5p340, University of California Transportation Center.
    8. Cheng, Long & Chen, Xuewu & Yang, Shuo & Cao, Zhan & De Vos, Jonas & Witlox, Frank, 2019. "Active travel for active ageing in China: The role of built environment," Journal of Transport Geography, Elsevier, vol. 76(C), pages 142-152.
    9. Pucher, J. & Buehler, R. & Bassett, D.R. & Dannenberg, A.L., 2010. "Walking and cycling to health: A comparative analysis of city, state, and international data," American Journal of Public Health, American Public Health Association, vol. 100(10), pages 1986-1992.
    10. Khan, Mobashwir & M. Kockelman, Kara & Xiong, Xiaoxia, 2014. "Models for anticipating non-motorized travel choices, and the role of the built environment," Transport Policy, Elsevier, vol. 35(C), pages 117-126.
    11. Yang, Hongtai & Lu, Xiaozhao & Cherry, Christopher & Liu, Xiaohan & Li, Yanlai, 2017. "Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression," Journal of Transport Geography, Elsevier, vol. 64(C), pages 184-194.
    12. Bueno, Paola Carolina & Gomez, Juan & Peters, Jonathan R. & Vassallo, Jose Manuel, 2017. "Understanding the effects of transit benefits on employees’ travel behavior: Evidence from the New York-New Jersey region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 99(C), pages 1-13.
    13. Jinhyun Hong & Qing Shen & Lei Zhang, 2014. "How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales," Transportation, Springer, vol. 41(3), pages 419-440, May.
    14. Yang, Linchuan & Chu, Xiaoling & Gou, Zhonghua & Yang, Hongtai & Lu, Yi & Huang, Wencheng, 2020. "Accessibility and proximity effects of bus rapid transit on housing prices: Heterogeneity across price quantiles and space," Journal of Transport Geography, Elsevier, vol. 88(C).
    15. Ton, Danique & Duives, Dorine C. & Cats, Oded & Hoogendoorn-Lanser, Sascha & Hoogendoorn, Serge P., 2019. "Cycling or walking? Determinants of mode choice in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 123(C), pages 7-23.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Eun Jung Kim & Suin Jin, 2023. "Walk Score and Neighborhood Walkability: A Case Study of Daegu, South Korea," IJERPH, MDPI, vol. 20(5), pages 1-12, February.
    2. Yang, Yongjiang & Sasaki, Kuniaki & Cheng, Long & Tao, Sui, 2022. "Does the built environment matter for active travel among older adults: Insights from Chiba City, Japan," Journal of Transport Geography, Elsevier, vol. 101(C).
    3. Qinglin Jia & Tao Zhang & Long Cheng & Gang Cheng & Minjie Jin, 2022. "The Impact of the Neighborhood Built Environment on the Walking Activity of Older Adults: A Multi-Scale Spatial Heterogeneity Analysis," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    4. Mohammad Paydar & Asal Kamani Fard, 2022. "Walking Behavior of Older Adults in Temuco, Chile: The Contribution of the Built Environment and Socio-Demographic Factors," IJERPH, MDPI, vol. 19(22), pages 1-23, November.
    5. Marek Więckowski, 2021. "Will the Consequences of Covid-19 Trigger a Redefining of the Role of Transport in the Development of Sustainable Tourism?," Sustainability, MDPI, vol. 13(4), pages 1-15, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Jixiang & Wang, Bo & Xiao, Longzhu, 2021. "Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach," Journal of Transport Geography, Elsevier, vol. 92(C).
    2. Eldeeb, Gamal & Mohamed, Moataz & Páez, Antonio, 2021. "Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice," Journal of Transport Geography, Elsevier, vol. 96(C).
    3. Faizeh Hatami & Jean-Claude Thill, 2022. "Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    4. Bereitschaft, Bradley, 2020. "Gentrification and the evolution of commuting behavior within America's urban cores, 2000–2015," Journal of Transport Geography, Elsevier, vol. 82(C).
    5. Yang, Yongjiang & Sasaki, Kuniaki & Cheng, Long & Tao, Sui, 2022. "Does the built environment matter for active travel among older adults: Insights from Chiba City, Japan," Journal of Transport Geography, Elsevier, vol. 101(C).
    6. Chowdhury, Tufayel & Scott, Darren M., 2020. "An analysis of the built environment and auto travel in Halifax, Canada," Transport Policy, Elsevier, vol. 94(C), pages 23-33.
    7. Ding, Chuan & Wang, Donggen & Liu, Chao & Zhang, Yi & Yang, Jiawen, 2017. "Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 65-80.
    8. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    9. Liu, Jixiang & Xiao, Longzhu, 2023. "Non-linear relationships between built environment and commuting duration of migrants and locals," Journal of Transport Geography, Elsevier, vol. 106(C).
    10. Hosseinzadeh, Aryan & Algomaiah, Majeed & Kluger, Robert & Li, Zhixia, 2021. "Spatial analysis of shared e-scooter trips," Journal of Transport Geography, Elsevier, vol. 92(C).
    11. Arefeh Nasri & Lei Zhang, 2019. "How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    12. Cheng, Long & Shi, Kunbo & De Vos, Jonas & Cao, Mengqiu & Witlox, Frank, 2021. "Examining the spatially heterogeneous effects of the built environment on walking among older adults," Transport Policy, Elsevier, vol. 100(C), pages 21-30.
    13. Huo, Jinghai & Yang, Hongtai & Li, Chaojing & Zheng, Rong & Yang, Linchuan & Wen, Yi, 2021. "Influence of the built environment on E-scooter sharing ridership: A tale of five cities," Journal of Transport Geography, Elsevier, vol. 93(C).
    14. Shen, Qing & Chen, Peng & Pan, Haixiao, 2016. "Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 31-44.
    15. Qinglin Jia & Tao Zhang & Long Cheng & Gang Cheng & Minjie Jin, 2022. "The Impact of the Neighborhood Built Environment on the Walking Activity of Older Adults: A Multi-Scale Spatial Heterogeneity Analysis," Sustainability, MDPI, vol. 14(21), pages 1-20, October.
    16. Mitra, Suman & Yao, Mingqi & Ritchie, Stephen G., 2021. "Gender differences in elderly mobility in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 203-226.
    17. Du, Mingyang & Cheng, Lin & Li, Xuefeng & Liu, Qiyang & Yang, Jingzong, 2022. "Spatial variation of ridesplitting adoption rate in Chicago," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 13-37.
    18. Zuo, Ting & Wei, Heng & Liu, Hao & Yang, Y. Jeffrey, 2019. "Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand," Journal of Transport Geography, Elsevier, vol. 74(C), pages 161-172.
    19. Wu, Guoqiang & Hong, Jinhyun, 2022. "An analysis of the role of residential location on the relationships between time spent online and non-mandatory activity-travel time use over time," Journal of Transport Geography, Elsevier, vol. 102(C).
    20. Dėdelė, Audrius & Miškinytė, Auksė & Andrušaitytė, Sandra & Nemaniūtė-Gužienė, Jolanta, 2020. "Dependence between travel distance, individual socioeconomic and health-related characteristics, and the choice of the travel mode: a cross-sectional study for Kaunas, Lithuania," Journal of Transport Geography, Elsevier, vol. 86(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:20:p:8752-:d:432653. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.